QCC: a novel clustering algorithm based on Quasi-Cluster Centers

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• 作者：Jinlong Huang ; Qingsheng Zhu ; Lijun Yang ; Dongdong Cheng ; Quanwang Wu
• 关键词：Clustering ; Center ; Similarity ; Neighbor ; Manifold
• 刊名：Machine Learning
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：106
• 期：3
• 页码：337-357
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Computing Methodologies; Simulation and Modeling; Language Translation and Linguistics;
• 出版者：Springer US
• ISSN：1573-0565
• 卷排序：106

文摘

Cluster analysis aims at classifying objects into categories on the basis of their similarity and has been widely used in many areas such as pattern recognition and image processing. In this paper, we propose a novel clustering algorithm called QCC mainly based on the following ideas: the density of a cluster center is the highest in its K nearest neighborhood or reverse K nearest neighborhood, and clusters are divided by sparse regions. Besides, we define a novel concept of similarity between clusters to solve the complex-manifold problem. In experiments, we compare the proposed algorithm QCC with DBSCAN, DP and DAAP algorithms on synthetic and real-world datasets. Results show that QCC performs the best, and its superiority on clustering non-spherical data and complex-manifold data is especially large.